Introduction To Predictive Analytics Part I

10,091
-1

Published on

Using Predictive Analytics to Increase Profitability - Part I

Introduction To Predictive Analytics Part I

  1. 1. Introduction to Predictive Analytics – Part I Jay Roy Chief Strategy Officer May 2011 | Dallas, TX
  2. 2. Table of Contents …  Definition of Analytics and Predictive Analytics  How Analytics and Predictive Analytics Compare  Defining Business Intelligence “BI” and its Relationship to Predictive Analytics Business Intelligence’s Evolution & its Organizational Impact The Importance of Communication Skills & Predictive Analytics The Business Case for Predictive Analytics  Conclusion and Key Takeaways
  3. 3. Definition of Analytics & Predictive Analytics
  4. 4. What is Analytics? Using analytics is like driving your car but watching traffic through the rear- view mirror, not seeing what’s ahead and thereby in danger of crashing “… the application of computer technology, operations research and statistics to solve problems in business and industry. Analytics is carried out within an information system.” “… the application of computer technology, operations research and statistics to solve problems in business and industry. Analytics is carried out within an information system.” Tom Davenport noted author
  5. 5. What is Predictive Analytics? Using predictive analytics is like driving your car and watching traffic through the front windshield, anticipating traffic, making course corrections to avoid traffic jams and getting there faster and safer “predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.” “Any solution that supports the identification of meaningful patterns and correlations among variables in complex, structured and unstructured, historical, and potential future data sets for the purposes of predicting future events and assessing the attractiveness of various courses of action.”
  6. 6. How Analytics & Predictive Analytics Compare
  7. 7. How Analytics and Predictive Analytics Compare  Predictive Analytics are more sophisticated analytics that “forward thinking” in nature Analytics is the understanding of existing (retrospective) data with the goal of understanding trends via comparison  Developing analytics is the first step towards deriving predictive analytics  They used for gaining insights from mathematical and/or financial modeling by enhancing understanding, interpretation and judgment for the purpose of good decision making
  8. 8. How Analytics and Predictive Analytics Compare Attribute Analytics Predictive Analytics Purpose: Understand the Past Observe Trends Catalyst for Discussion Gain Insights Make Decisions Take Action View: Historical and Current Future Oriented Metrics Type: Lagging Indicators Leading Indicators Data Used: Raw & Compiled Information Data Type: Structured Structured and Unstructured Users: Middle & Senior Mgt Analysts, End Users C-Level & Senior Mgt Strategists, Analysts, Mgrs Benefits: Gaining an understanding of data Productivity Improvements Gaining Information & Insights Process Improvements
  9. 9. Benefits of Analytics and Predictive Analytics  Benefits of analytics: productivity gains through improved data-gathering processes results in less time required for producing reports and metrics  Takeaway: Both types of gains are beneficial but improvements in analytics are NOT as scalable as to the benefits in predictive analytics which are repeatable, virtuous and scalable  Benefits of predictive analytics: process improvement gains through improve revenue generation & cost structures leading to enhanced decision making
  10. 10. Defining Business Intelligence “BI” & its Relationship to Predictive Analytics
  11. 11. Defining Business Intelligence & its Relationship to Predictive Analytics Unfortunately, the human & business strategy elements are often overlooked and forgotten but are key ingredients to the success of BI “… computer-based techniques used in identifying, extracting and analyzing business data … aims to support better business decision- making … BI technologies provide historical, current and predictive views of business operations.”  BI is typically thought of in terms of technology inclusive of data management practices, data warehouses, ETL processes, etc.  Predictive Analytics are a sub-set of Analytics and a branch of BI which is the least understood and underestimated
  12. 12. Defining Business Intelligence & its Relationship to Predictive Analytics Analytics serves as the “glue” in aligning the key elements of business Analytics provide the feedback to business people signaling success or failure of their strategy and business model  Business Intelligence = Business + Intelligence Business = The Strategy + Business Model + Infrastructure + Technology + + +
  13. 13. Defining Business Intelligence & its Relationship to Predictive Analytics  People create information for the organization in order to gain understanding of its customers, competitors and ecosystem  Business Intelligence is a process of generating insights and or knowledge (predictive analytics) through people and technologies in order to execute their strategy  This process needs to be leveraged into a core competency, a unique and virtuous process to differentiate the business in a world of “me-too” organizations & strategies Intelligence = People + Processes + Analytics + +=
  14. 14. Business Intelligence’s Evolution & its Organizational Impact
  15. 15. BI’s Evolution and its Organizational Impact The most important part of BI is the human element and achieving people’s business and personal goals  Most businesses organize their BI activities and professionals under the IT function under the Enterprise 1.0 model  With advances in technology and social media, the Enterprise 1.0 model, is not the most efficient, scalable, and collaborative way to execute your business strategy especially from a human resourcing perspective  With globalization, advances in internet technologies and social media, we have advanced to the era of Enterprise 2.5  As a result of Enterprise 2.5, changes in business require evolution in BI
  16. 16. BI’s Evolution & its Organizational (Design) Impacts  In the era of Enterprise 2.5, BI is readily becoming a distinctive capability & asset for organizations  If BI is deemed strategic, this function should be realigned to fall under the direction of the CEO or Office of Strategy Management (OSM)  Implementing a new organizational structure will encounter language and communication challenges between business and BI professionals CEO CIO Business Intelligence Group CEO COO CIO Office of Strategy Management & Business Intelligence Group Old Model – “Enterprise 1.0” New Model – “Enterprise 2.5”
  17. 17. The Importance of Communication Skills & Predictive Analytics
  18. 18. The Importance of Communication Skills & Predictive Analytics  The purpose of predictive analytics is to help organizations see relationships between business elements so senior management may craft targeted business strategies and exploit opportunities on a timely basis with a focus on the future  In order to benefit from predictive analytics, people across the organization must communicate and understand with one another but language often becomes a barrier  BI professionals often think language is SQL (Structured Query Language) and business people often think language is reports, metrics and meetings  IT & BI professionals need to understand the language of strategy, business models and performance while solving business not technology problems SQL vs
  19. 19. The Importance of Communication Skills & Predictive Analytics Need market segmentation report, now! OK, what are the parameters and how do you want it rendered? CEO/Business People BI People Conversations @ Work
  20. 20. The Importance of Communication Skills & Predictive Analytics Huh? What is he asking me? Just need my report! CEO/Business People Huh? What is he asking me? Market Segmentation? BI People Conversations @ Work
  21. 21. The Importance of Communication Skills & Predictive Analytics Takeaway: Business professionals need to appreciate the role of technology as an enabler and they need to lead and determine where & how IT/BI infrastructure should be deployed to improve decision making  Takeaway: It is not enough to have state of the art in BI technologies, without having a common understanding and a common language between the business people and BI professionals, otherwise BI efforts will fall short of desired results Takeaway: IT & BI professionals need to understand the language of strategy, business models and performance while solving business NOT technology problems
  22. 22. The Business Case for Predictive Analytics
  23. 23. The Business Case for Predictive Analytics – Macro level  On a macro level, organizations need predictive analytics for:  Strategic Planning  Financial Planning  Focusing on Priorities  Competitive Analyses  Achieving Profit and Revenue Targets  Developing Competitive Advantages and Differentiation  Predictive analytics can provide timely feedback to executives on their strategic initiatives – without feedback course corrections may be too late  Predictive analytics provide leading indicators and insight to assist in planning for answering the big question: What should we do next? – next quarter, next year etc.
  24. 24. The Business Case for Predictive Analytics – Micro level  On a micro level, organizations need predictive analytics for:  Improving business processes  Doing more with less budget (working smarter not harder!)  Allocating resources appropriately  Understanding correlations and sensitivities with customer segments  To ensure long term financial resources are available to run the business  Developing Competitive Advantages and Differentiation  Q: Why do most organizations struggle with Analytics and especially Predictive Analytics?  A: Organizations fail to recognize and misunderstand the necessary and intangible elements of people, skills, and corporate culture and tying these elements back to their analytics, business model and strategies – Caution: this is a long-term fix
  25. 25. Conclusion & Key Takeaways
  26. 26. Conclusion & Key Takeaways Takeaway: Predictive Analytics is the analytical ability to see relationships between business drivers and performance and the ability to model these relationships performed by people to improve organizational visibility  Conclusion: Business Intelligence begins with your organization’s strategy and business model and only then should performance metrics and analytics be appropriately conceived and deployed Takeaway: It is not enough to have state of the art in BI technologies, without having a common understanding and a common language between the business people and BI professionals, otherwise BI efforts will fall short of desired results
  27. 27. Conclusion & Key Takeaways Takeaway: IT & BI professionals need to understand the language of strategy, business models and performance while solving business not technology problems  Takeaway: IT & BI professionals need to understand the language of strategy, business models and performance while solving business not technology problems Takeaway: Business professionals need to appreciate the role of technology as an enabler and they need to lead and determine where & how IT/BI infrastructure should be deployed to improve decision making
  28. 28. Sources, References, and Trade Marks  www.wikipedia.org  Competing on Analytics, 2007, Thomas H. Davenport  www.forrester.com The Lego Minifigure is a trade mark of The Lego Group Clipart provided by OCAL and www.clker.com
  29. 29. Introduction to Predictive Analytics – Part I Jay Roy, Chief Strategy Officer www.predictivedashboards.com jay.roy@predictivedashboards.com T:214-621-7612

×